This application addresses broad Challenge Area (03) Biomarker Discovery and Validation and specific Challenge Topic 03-MH-101*: Biomarkers in Mental Disorders. The overall goal of this proposal is to advance the development of behavioral and genetic biomarkers for autism and related disorders. While it is clear that autism has a strong inherited genetic component, very large scale genetic studies that have relied only on a general diagnosis of autism (spectrum) disorders (or other information only on affected individuals) have had limited success in identifying risk alleles, leaving a critical issue for the field. Clearly, alternative genetic study designs are needed to complement existing studies. Behavioral biomarkers, especially language ability, have been used with some success to increase power in gene mapping, but to date studies have focused on detailed behavioral assessments only of subjects with autism and not their family members, despite an extensive literature defining increased rates of related phenotypes in family members. This critical gap will be filled by our project. We will use our existing family set with an extensive existing database of clinical and genetic data from all family members, where each family contains at least one proband with autism and at least one proband with a language deficit, to define biomarkers for risk. For Aim 1, Behavioral Biomarker Development, we will develop a set of behavioral biomarkers of genetic risk for autism and related disorders. We will analyze our extensive behavioral testing database to determine which measures have the strongest genetic effects and further examine latent class structures for both data reduction and to reduce measurement error. We will conduct follow-up assessments on a subset of study participants to determine longitudinal stability of selected biomarkers. For Aim 2, Behavioral Biomarker Validation, we will validate inherited components of the behavioral biomarkers through the use of genome-wide analysis. We will use analysis of quantitative and dichotomous behavioral biomarker data using a quasi-Bayesian posterior probability method to elucidate the genetic architecture of risk, providing evidence of the nature of the inherited genetic component. For Aim 3, Genetic Biomarker Identification, we will identify specific DNA variations associated with genetic risk for autism and related disorders. We will test both common and rare variants, SNPs and CNVs, from candidate genes within the regions identified in Aim 2 and evaluate additional variants from the literature as potential factors modulating a network of risk-determining genes. Overall, we plan to combine analysis of behavioral and genetic biomarkers to develop more accurate models for the prediction of risk for autism spectrum disorders. Our existing detailed clinical and behavioral information, as well as plans for follow-up assessments, will also provide important preliminary data for future comparative effectiveness studies on elements of clinical course and treatment response related to specific biomarkers. It is hoped that these studies will provide substantive insights into the causes of, and effective treatments for, autism. Autism is a serious and debilitating disorder. While there is strong evidence supporting a significant genetic component to the disorder, the identification of specific susceptibility genes has been difficult. Identification of susceptibility genes through the approaches proposed could provide important insights into biological basis of this illness, which could result in the development of novel treatments.